Create main.py

main.py

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+
+import io
+from flask import Flask, request
+from twilio.twiml.messaging_response import MessagingResponse
+from langchain.llms import GooglePalm
+import pandas as pd
+#from yolopandas import pd
+import os
+from langchain.embeddings import GooglePalmEmbeddings
+# a class to create a question answering system based on information retrieval
+from langchain.chains import RetrievalQA
+# a class for splitting text into fixed-sized chunks with an optional overlay
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+# a class to create a vector index using FAISS, a library for approximate nearest neighbor search
+from langchain.vectorstores import FAISS
+# a class for loading PDF documents from a directory
+from langchain.document_loaders import PyPDFDirectoryLoader
+from langchain.chains.question_answering import load_qa_chain
+from langchain.chains import ConversationalRetrievalChain
+from langchain.schema.vectorstore import VectorStoreRetriever
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+
+
+def get_pdf_text(pdf_docs):
+    text=""
+    for pdf in pdf_docs:
+        pdf_reader= PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text+= page.extract_text()
+    return  text
+
+# load PDF files from a directory
+loader = PyPDFDirectoryLoader("documents/")
+data = loader.load()
+
+# print the loaded data, which is a list of tuples (file name, text extracted from the PDF)
+#print(data)
+
+# split the extracted data into text chunks using the text_splitter, which splits the text based on the specified number of characters and overlap
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=20)
+
+text_chunks = text_splitter.split_documents(data)
+
+# print the number of chunks obtained
+#print(len(text_chunks))
+
+embeddings = GooglePalmEmbeddings(google_api_key=os.environ['PALM'])
+
+# create embeddings for each text chunk using the FAISS class, which creates a vector index using FAISS and allows efficient searches between vectors
+vector_store = FAISS.from_documents(text_chunks, embedding=embeddings)
+
+#print(type(vector_store))
+
+
+
+def ask_pdfs(user_question):
+    load_dotenv()
+
+
+
+    llm = GooglePalm(temperature=0, google_api_key=os.environ['PALM'])
+
+    # Create a question answering system based on information retrieval using the RetrievalQA class, which takes as input a neural language model, a chain type and a retriever (an object that allows you to retrieve the most relevant chunks of text for a query)
+    retriever = VectorStoreRetriever(vectorstore=vector_store)
+    qa = RetrievalQA.from_llm(llm=llm, retriever=retriever)
+    response =qa.run(user_question)
+    #print("Response:",response)
+
+    return response
+
+app = Flask(__name__)
+
+@app.route("/bot", methods=["POST"])
+def whatsapp():
+
+    # user input
+    user_msg = request.values.get('Body', '').lower()
+
+    # creating object of MessagingResponse
+    response = MessagingResponse()
+
+    # User Query
+    q = user_msg
+
+    response = ask_pdfs(q)
+
+    return str(response)
+
+
+if __name__ == "__main__":
+    app.run()